The DSOM algorithm of [1]} is compared to ours (VDSOM) on 20000
samples of the MNIST Fashion dataset on a \(10 \times 10\)  toroidal grid. DSOM
uses a learning rate of \(\alpha =10^{-3}\)  while VDSOM uses the same configuration
as before. In Fig. REF , the distorsion is plotted as a
function of the elasticity \(\eta \) . VDSOM outperforms DSOM while being less
sensitive. In Fig. REF , the weights of both methods trained with their
respective optimal elasticity are displayed on a grid. Note that some of the
DSOM weights are noise while this is not the case for VDSOM. VDSOM weights also
seem less blurred.
